Loss Functions

Regularized estimation of image statistics by Score Matching NeurIPS 2010 Relaxed Clipping: A Global Training Method for Robust Regression and Classification NeurIPS 2010 Variable margin losses for classifier design NeurIPS 2010 Distribution-Calibrated Hierarchical Classification NeurIPS 2009 From PAC-Bayes Bounds to KL Regularization NeurIPS 2009 Learning Nondeterministic Classifiers JMLR 2009 Learning to Rank by Optimizing NDCG Measure NeurIPS 2009 Ranking Measures and Loss Functions in Learning to Rank NeurIPS 2009 On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost NeurIPS 2008 On the Efficient Minimization of Classification Calibrated Surrogates NeurIPS 2008 Support Vector Machines with a Reject Option NeurIPS 2008 Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression JMLR 2007 How SVMs can estimate quantiles and the median NeurIPS 2007 Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data JMLR 2007 Stagewise Lasso JMLR 2007 Considering Cost Asymmetry in Learning Classifiers JMLR 2006 Learning to Rank with Nonsmooth Cost Functions NeurIPS 2006 Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis JMLR 2006 Smooth ε-Insensitive Regression by Loss Symmetrization JMLR 2005 On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition JMLR 2004